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FFT based ensembled model to predict ranks of higher educational institutions

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Abstract

Predicting international rankings has always been a demanding area for Universities and Higher Educational Institutions (HEIs) all over the world in the recent decade. In this research work, a novel tool EnFftRP (Ensembled Fast Fourier Transformed Ranking Prediction) is developed for predicting international ranks of various universities and HEIs. It uses a hybrid ensembled model in duology with the Fast Fourier Transformation (FFT). Ensemble model improves the prediction accuracies which are elevated further using FFT. The fourier processing algorithm, being an influential computational concept for data anatomy is a novel approach applied to the ensembled model. A combination of six base models Decision Tree, Support Vector Machine, Multilayer Perceptron, K-Nearest Neighbour, Random Forest and Logistic Regression are deployed for the construction of ensembled model. The data set being used is Shanghai World Ranking University Dataset for 14 years ranging from 2005 to 2018. It is split into training and test data set. The training data set is considered from year 2005–2014 and the test dataset from 2015 to 2018. It is empirically established that proposed tool produces highly promising prediction parameters as accuracies (95%), specificities (94.41%), sensitivities (95.54%), Productively Predicted Values (94.94%), Non-Productively Predicted Values (95.07%), F1-score (97.40%) and Kappa score (0.90) as compared to obtained by similar models like RSFT and others. To the best of our knowledge, till now no tool exists which can predict the ranks of HEIs with this much high predictive power.

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Data availability

The major dataset i e Shanghai ranking dataset from (2005–2015) which is analysed during the current study is available in the (data world) repository, https://query data world/s/g3lmu2afosoprs6fxnoe5eqfjcfric

Shanghai ranking dataset from (2016–2018) is added by authors from Shanghai world university rankings, www.Shanghairanking.com

Code availability

All the code is available with the authors in Python language embedded in Google Collaboratory Sheet

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  1. Computer Science and Engineering, IGDTUW, Delhi, India

    Nidhi Agarwal & Devendra K. Tayal

  2. KIET Group Of Institutions, Delhi-NCR, Ghaziabad, Uttar Pradesh, India

    Nidhi Agarwal

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All authors whose names appear on the submission have made substantial contributions to the conception or design of the work, its acquisition, analysis and to the interpretation of data They have drafted the work and revised it critically for important intellectual content The authors have approved the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved

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Agarwal, N., Tayal, D.K. FFT based ensembled model to predict ranks of higher educational institutions. Multimed Tools Appl 81, 34129–34162 (2022). https://doi.org/10.1007/s11042-022-13180-9

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